We conduct experiments on the Email Campaign data [1]} whose results show the benefit of using the d-separation set from the causal graph. Our D-UCB incurs less regrets than two baselines, the classic UCB which does not leverage any causal information as well as the C-UCB.
In addition, we validate numerically that our F-UCB maintains good performance while satisfying counterfactual individual fairness in each round. On the contrary, the baselines fail to achieve fairness with significant percentages of recommendations violating fairness constraint. We further conduct experiments on the Adult-Video dataset and compare our F-UCB with another user-side fair bandit algorithm Fair-LinUCB [2]}. The results demonstrate the advantage of our causal based fair bandit algorithm on achieving individual level fairness in online recommendation.
